https://kforthman.shinyapps.io/500citiescounties/

#
#remove scientific notation
options(scipen=999)

—- Load packages —-

library(stringr)
library(corrplot)
## corrplot 0.84 loaded
library(shiny)
library(lme4)
## Loading required package: Matrix
library(lmerTest)
## 
## Attaching package: 'lmerTest'
## The following object is masked from 'package:lme4':
## 
##     lmer
## The following object is masked from 'package:stats':
## 
##     step

—- Load neighborhood data —-

load("Data/county_factors.rda")
load("Data/county_500CitiesData.rda")

—- Load and format covid data —-

data.path <- "Data/COVID-19/csse_covid_19_data/csse_covid_19_time_series/"

# Read in the data
US.deaths <- read.csv(
  paste0(data.path, "time_series_covid19_deaths_US.csv"), 
  header = T, stringsAsFactors = F)
US.cases <- read.csv(
  paste0(data.path, "time_series_covid19_confirmed_US.csv"), 
  header = T, stringsAsFactors = F)

# Read in the header seprately.
US.cases.head <- read.csv(
  paste0(data.path, "time_series_covid19_confirmed_US.csv"), 
  header = F, stringsAsFactors = F)[1,]
US.deaths.head <- read.csv(
  paste0(data.path, "time_series_covid19_deaths_US.csv"), 
  header = F, stringsAsFactors = F)[1,]

# Correct the dates in the header to be more useable as
# column names.
proper_date <- function(dates){
  dates <- sapply(dates, strsplit, split = "/")
  dates <- lapply(dates, str_pad, width = 2, side = "left", pad = "0")
  dates <- lapply(dates, paste, collapse = "_")
  dates <- unlist(dates)
  
  return(dates)
}

dates.cases <- proper_date(US.cases.head[-c(1:11)])
dates.deaths <- proper_date(US.deaths.head[-c(1:12)])

names(US.cases) <- c(US.cases.head[1,1:11], dates.cases)
names(US.deaths) <- c(US.deaths.head[1,1:12], dates.deaths)

if(sum(US.cases$UID != US.deaths$UID, na.rm = T) > 0){warning("COVID data rows do not match!")}
US.cases$Population <- US.deaths$Population
US.cases <- US.cases[,c(1:11, ncol(US.cases), 12:(ncol(US.cases)-1))]

Other stats within the daily reports

data.path <- "Data/COVID-19/csse_covid_19_data/csse_covid_19_daily_reports_us/"
daily_filenames <- list.files(data.path)
daily_filenames <- daily_filenames[daily_filenames != "README.md"]

todays_report_filename <- daily_filenames[length(daily_filenames)]
US.todaysReport <- read.csv(
  paste0(data.path, todays_report_filename), 
  header = T, stringsAsFactors = F)
all.states <- c('Alabama', 'Alaska', 'American Samoa', 'Arizona', 'Arkansas', 'California', 'Colorado', 'Connecticut', 'Delaware', 'Diamond Princess', 'District of Columbia', 'Florida', 'Georgia', 'Grand Princess', 'Guam', 'Hawaii', 'Idaho', 'Illinois', 'Indiana', 'Iowa', 'Kansas', 'Kentucky', 'Louisiana', 'Maine', 'Maryland', 'Massachusetts', 'Michigan', 'Minnesota', 'Mississippi', 'Missouri', 'Montana', 'Nebraska', 'Nevada', 'New Hampshire', 'New Jersey', 'New Mexico', 'New York', 'North Carolina', 'North Dakota', 'Northern Mariana Islands', 'Ohio', 'Oklahoma', 'Oregon', 'Pennsylvania', 'Puerto Rico', 'Rhode Island', 'South Carolina', 'South Dakota', 'Tennessee', 'Texas', 'Utah', 'Vermont', 'Virgin Islands', 'Virginia', 'Washington', 'West Virginia', 'Wisconsin', 'Wyoming')
all.states.df <- data.frame(Province_State = all.states)
all.stats <- c("Confirmed", "Deaths", "Recovered", "Active", "Incident_Rate", "People_Tested", "People_Hospitalized", "Mortality_Rate", "Testing_Rate", "Hospitalization_Rate")

compiled.stats <- list()
for(i in 1:length(daily_filenames)){
  day <- substring(daily_filenames[i],1,10)
  data <- read.csv(
    paste0(data.path, daily_filenames[i]),
    header = T, stringsAsFactors = F)
  compiled.stats[[i]] <- merge(all.states.df, data, all.y = F)
  names(compiled.stats)[i] <- day
}

Functions for compiling and visualizing stats in the daily reports.

plot.dailyStat <- function(state, stat){
  data <- sapply(1:length(daily_filenames), function(x){compiled.stats[[x]][compiled.stats[[x]]$Province_State == state, stat]})
  names(data) <- daily_filenames
  barplot(data, main = paste0(state, " ", stat), las = 2, cex.axis = 1, cex.names = 0.5)
}

plot.dailyStatRise <- function(state, stat){
  data <- sapply(1:length(daily_filenames), function(x){compiled.stats[[x]][compiled.stats[[x]]$Province_State == state, stat]})
  names(data) <- daily_filenames
  
  rise.stat <- matrix(ncol = length(data) - 1, nrow = 1)
  colnames(rise.stat) <- names(data)[-1]
  for(i in 1:ncol(rise.stat) + 1){
    rise <- data[i] - data[i-1]
    rise.stat[i-1] <- rise
  }
  
  barplot(rise.stat, main = paste0(state, " rise in ",stat), las = 2, cex.axis = 1, cex.names = 0.5)
}
testing.data.state <- compiled.stats[[length(daily_filenames)]][, c("Province_State", "Testing_Rate")]
testing.data.state <- testing.data.state[!is.na(testing.data.state$Testing_Rate),]
testing.data.state <- testing.data.state[order(testing.data.state$Testing_Rate),]

col.state <- rep("pink", nrow(testing.data.state))

avg.test.rate <- mean(testing.data.state$Testing_Rate, na.rm = T)

col.state[testing.data.state$Testing_Rate < avg.test.rate] <- "grey"
col.state[testing.data.state$Province_State == "Oklahoma"] <- "lightblue"

par(mar = c(5,6,4,2))
barplot(testing.data.state$Testing_Rate, names.arg = testing.data.state$Province_State, horiz = T, main = "Testing Rate by State", las = 2, cex.axis = 1, cex.names = 0.5, col = col.state, border = F, xlab = "Total number of people tested per 100,000 persons.")
abline(v = avg.test.rate, col = "red")
text(x = avg.test.rate + 10, y = 1, labels = "Average Testing Rate", adj = c(0, 0.5), col = "red")

Interactive Plots

Province_State - The name of the State within the USA. Country_Region - The name of the Country (US). Last_Update - The most recent date the file was pushed. Lat - Latitude. Long_ - Longitude. Confirmed - Aggregated confirmed case count for the state. Deaths - Aggregated Death case count for the state. Recovered - Aggregated Recovered case count for the state. Active - Aggregated confirmed cases that have not been resolved (Active = Confirmed - Recovered - Deaths). FIPS - Federal Information Processing Standards code that uniquely identifies counties within the USA. Incident_Rate - confirmed cases per 100,000 persons. People_Tested - Total number of people who have been tested. People_Hospitalized - Total number of people hospitalized. Mortality_Rate - Number recorded deaths * 100/ Number confirmed cases. UID - Unique Identifier for each row entry. ISO3 - Officialy assigned country code identifiers. Testing_Rate - Total number of people tested per 100,000 persons. Hospitalization_Rate - Total number of people hospitalized * 100/ Number of confirmed cases.

Split the dataset into the data and the info for usability.

US.cases.info <- as.matrix(US.cases[,1:12])
US.cases.data <- as.matrix(US.cases[,-c(2:12)])
US.deaths.info <- as.matrix(US.deaths[,1:12])
US.deaths.data <- as.matrix(US.deaths[,-c(2:12)])

rownames(US.cases.info) <- US.cases.info[,1]
US.cases.info <- US.cases.info[,-1]
rownames(US.cases.data) <- US.cases.data[,1]
US.cases.data <- US.cases.data[,-1]
rownames(US.deaths.info) <- US.deaths.info[,1]
US.deaths.info <- US.deaths.info[,-1]
rownames(US.deaths.data) <- US.deaths.data[,1]
US.deaths.data <- US.deaths.data[,-1]


ndays.cases <- ncol(US.cases.data)
ndays.deaths <- ncol(US.deaths.data)

nobs <- nrow(US.cases.data)

—- The Curve —-

state.curve <- function(state, stat = c("cases", "deaths"), logScale = T){
  if(stat == "cases"){
    data <- US.cases.data[which(US.cases$Province_State == state),]
  }else if(stat == "deaths"){
    data <- US.deaths.data[which(US.deaths$Province_State == state),]
  }
  data.sum <- colSums(data)
  day.first.case <- min(which(data.sum > 0))
  n.days <- length(data.sum)
  
  if(logScale == T){
    barplot(data.sum[day.first.case:n.days], 
            main = paste0("Total COVID-19 ", stat," by date in ", state, ", log scale"), 
            log = "y", las = 2, cex.axis = 1, cex.names = 0.5)
  }else{
    barplot(data.sum[day.first.case:n.days], 
            main = paste0("Total COVID-19 ", stat," by date in ", state), 
            las = 2, cex.axis = 1, cex.names = 0.5)
  }
}
state.rise <- function(state, stat = c("cases", "deaths")){
  if(stat == "cases"){
    data.thisState <- US.cases.data[which(US.cases$Province_State == state),]
  }else if(stat == "deaths"){
    data.thisState <- US.deaths.data[which(US.deaths$Province_State == state),]
  }
  
  data.sum <- colSums(data.thisState)
  n.days <- ncol(data.thisState)
  
  rise.cases <- matrix(ncol = n.days - 1, nrow = 1)
  colnames(rise.cases) <- colnames(data.thisState)[-1]
  for(i in 1:ncol(rise.cases) + 1){
    rise <- data.sum[i] - data.sum[i-1]
    rise.cases[i-1] <- rise
  }
  
  day.first.case <- min(which(rise.cases > 0))
  n.days <- length(rise.cases)
  
  barplot(rise.cases[,day.first.case:n.days], main = paste0("Rise in COVID-19 ", stat, " by Date in ", state), las = 2, cex.axis = 1, cex.names = 0.5)
}
county.curve <- function(county, stat = c("cases", "deaths")){
  if(stat == "cases"){
    data <- US.cases.data[which(US.cases$Admin2 == county),]
  }else if(stat == "deaths"){
    data <- US.deaths.data[which(US.deaths$Admin2 == county),]
  }
  
  day.first.case <- min(which(data > 0))
  n.days <- length(data)
  
  barplot(data[day.first.case:n.days], main = paste0("Total COVID-19 ", stat," by date in ", county), log = "y", las = 2, cex.axis = 1, cex.names = 0.5)
  
}

county.curve("Tulsa", "cases")

county.curve("Tulsa", "deaths")

—- Calculate some useful stats to compare with neighborhood data —-

US.stats <- data.frame(UID = US.cases$UID)
cases.total <- colSums(US.cases.data)

day.first.case <- min(which(cases.total > 100))
n.days <- length(cases.total)

par(mar = c(5,5,4,2))
barplot(cases.total[day.first.case:n.days], main = "Total COVID-19 cases by Date in US", las = 2, cex.axis = 1, cex.names = 0.5)

barplot(cases.total[day.first.case:n.days], main = "Total COVID-19 cases by Date in US, log scale", las = 2, cex.axis = 1, cex.names = 0.5, log = "y")

deaths.total <- colSums(US.deaths.data)

day.first.case <- min(which(deaths.total > 0))
n.days <- length(deaths.total)

barplot(deaths.total[day.first.case:n.days], main = "Total COVID-19 deaths by Date in US", las = 2, cex.axis = 1, cex.names = 0.5)

barplot(deaths.total[day.first.case:n.days], main = "Total COVID-19 deaths by Date in US, log scale", las = 2, cex.axis = 1, cex.names = 0.5, log = "y")

Average rise in cases per day

avg.rise.cases

rise.cases <- matrix(ncol = ndays.cases - 1, nrow = nobs)
colnames(rise.cases) <- colnames(US.cases.data)[-1]
for(i in 1:ncol(rise.cases) + 1){
  rise <- US.cases.data[,i] - US.cases.data[,i-1]
  rise.cases[,i-1] <- rise
}

US.stats$avg.rise.cases <- apply(rise.cases, 1, mean)

rise.cases.total <- colSums(rise.cases)

day.first.case <- min(which(rise.cases.total > 0))
n.days <- length(rise.cases.total)

barplot(rise.cases.total[day.first.case:n.days], main = "Rise in Cases of COVID-19 by Date in US", las = 2, cex.axis = 1, cex.names = 0.5)

Average rise in deaths per day

avg.rise.deaths

rise.deaths <- matrix(ncol = ndays.deaths - 1, nrow = nobs)
colnames(rise.deaths) <- colnames(US.deaths.data)[-1]
for(i in 1:ncol(rise.deaths) + 1){
  rise <- US.deaths.data[,i] - US.deaths.data[,i-1]
  rise.deaths[,i-1] <- rise
}

US.stats$avg.rise.deaths <- apply(rise.deaths, 1, mean)

rise.deaths.total <- colSums(rise.deaths)

day.first.case <- min(which(rise.deaths.total > 0))
n.days <- length(rise.deaths.total)

barplot(rise.deaths.total[day.first.case:n.days], main = "Rise in Deaths of COVID-19 by Date in US", las = 2, cex.axis = 1, cex.names = 0.5)

Total cases

total.cases

US.stats$total.cases <- US.cases.data[,ndays.cases]

Total cases per capita

US.stats$total.cases.percap <- US.stats$total.cases / US.cases$Population
US.stats$total.cases.percap[US.cases$Population == 0] <- NA
hist(US.stats$total.cases.percap)

Total deaths

total.deaths

US.stats$total.deaths <- US.deaths.data[,ndays.deaths]

Total deaths per capita

total.deaths.percap

US.stats$total.deaths.percap <- US.stats$total.deaths / US.deaths$Population
US.stats$total.deaths.percap[US.deaths$Population == 0] <- NA

max(US.stats$total.deaths.percap,na.rm = T)
## [1] 0.003283872

Total deaths per case

total.deaths.percase Error in Johns Hopkins data has rows with total.deaths > total.cases.

# pos.case.ind <- US.stats$total.cases > 0
# US.stats$total.deaths.percase[pos.case.ind] <- US.stats$total.deaths[pos.case.ind] / US.stats$total.cases[pos.case.ind]
# US.stats$total.deaths.percase[!pos.case.ind] <- 0
US.stats$total.deaths.percase <- US.stats$total.deaths / US.stats$total.cases
US.stats$total.deaths.percase[US.stats$total.cases == 0] <- NA

US.stats[which(US.stats$total.deaths > US.stats$total.cases),]
##           UID avg.rise.cases avg.rise.deaths total.cases
## 3155 84080008      0.0000000      0.01941748           0
## 3204 84090004      0.0000000      0.33009709           0
## 3206 84090006      0.0000000      0.01941748           0
## 3220 84090022      0.6116505      0.70873786          63
## 3222 84090024      0.0000000      0.74757282           0
## 3229 84090031      0.0000000      0.10679612           0
## 3230 84090032      0.0000000      0.02912621           0
## 3231 84090033      0.1456311      0.25242718          15
## 3233 84090035      0.0000000      0.05825243           0
## 3236 84090038      0.0000000      0.05825243           0
## 3252 84090056      0.0000000      0.05825243           0
##      total.cases.percap total.deaths total.deaths.percap
## 3155                 NA            2                  NA
## 3204                 NA           34                  NA
## 3206                 NA            2                  NA
## 3220                 NA           73                  NA
## 3222                 NA           77                  NA
## 3229                 NA           11                  NA
## 3230                 NA            3                  NA
## 3231                 NA           26                  NA
## 3233                 NA            6                  NA
## 3236                 NA            6                  NA
## 3252                 NA            6                  NA
##      total.deaths.percase
## 3155                   NA
## 3204                   NA
## 3206                   NA
## 3220             1.158730
## 3222                   NA
## 3229                   NA
## 3230                   NA
## 3231             1.733333
## 3233                   NA
## 3236                   NA
## 3252                   NA

—- Merge COVID data with Neighborhood data —-

US.stats$ID <- str_pad(US.stats$UID, 8, "left", pad = "0")
US.stats$ID <- substr(US.stats$ID, 4, 8)

data.merge <- merge(US.stats, county_factors, by = "ID")

—- Plot the relationships —-

data.cor <- cor(data.merge[,-c(1:2)], use = "complete.obs", method = "spearman")
corrplot.mixed(data.cor, upper = 'ellipse', lower = 'number', tl.pos = 'lt', tl.cex = 1, lower.col = "black", number.cex = 0.5)

data.merge2 <- merge(data.merge, county_500CitiesData, by = "ID", all.x = F)

—- Plot the relationships —-

data.cor2 <- cor(data.merge2[,-c(1:2)], use = "complete.obs", method = "spearman")
corrplot.mixed(data.cor2, upper = 'ellipse', lower = 'number', tl.pos = 'lt', tl.cex = 1, lower.col = "black", number.cex = 0.5)

corrplot.mixed(data.cor2[1:7,8:42], upper = 'ellipse', lower = 'number', tl.pos = 'lt', tl.cex = 1, lower.col = "black", number.cex = 0.5)

—-Linear Mixed Effects Model —-

US.todaysReport.states <- US.todaysReport[!is.na(US.todaysReport$FIPS) & nchar(US.todaysReport$FIPS)<=2,]
US.todaysReport.states$FIPS <- str_pad(US.todaysReport.states$FIPS, 2, "left", pad = "0")

data.merge2$stateID <- substr(data.merge2$ID,1,2)

data.merge3 <- merge(data.merge2, US.todaysReport.states, by.x = "stateID", by.y = "FIPS")

county.Demo_and_Covid <- data.merge3
save(county.Demo_and_Covid, file = "county.Demo_and_Covid.Rda")

this.lme <- lmer("total.cases.percap ~ Affluence + Singletons.in.Tract + Seniors.in.Tract + African.Americans.in.Tract + Noncitizens.in.Tract + High.BP + Binge.Drinking + Cancer + Asthma + Heart.Disease + COPD + Smoking + Diabetes + No.Physical.Activity + Obesity + Poor.Sleeping.Habits + Poor.Mental.Health + Testing_Rate + Hospitalization_Rate + (1 | stateID)", data = data.merge3)
## Warning: Some predictor variables are on very different scales: consider
## rescaling

## Warning: Some predictor variables are on very different scales: consider
## rescaling
print(summary(this.lme), correlation=TRUE)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: 
## "total.cases.percap ~ Affluence + Singletons.in.Tract + Seniors.in.Tract + African.Americans.in.Tract + Noncitizens.in.Tract + High.BP + Binge.Drinking + Cancer + Asthma + Heart.Disease + COPD + Smoking + Diabetes + No.Physical.Activity + Obesity + Poor.Sleeping.Habits + Poor.Mental.Health + Testing_Rate + Hospitalization_Rate + (1 | stateID)"
##    Data: data.merge3
## 
## REML criterion at convergence: -1138.4
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.7683 -0.3341 -0.0919  0.1782  5.7641 
## 
## Random effects:
##  Groups   Name        Variance     Std.Dev. 
##  stateID  (Intercept) 0.0000007021 0.0008379
##  Residual             0.0000126053 0.0035504
## Number of obs: 169, groups:  stateID, 32
## 
## Fixed effects:
##                                  Estimate     Std. Error             df
## (Intercept)                 -0.0078746855   0.0090298040  62.3138355044
## Affluence                    0.0043698425   0.0010519500  91.5326788737
## Singletons.in.Tract          0.0017059986   0.0009150532 131.4593204704
## Seniors.in.Tract             0.0009375375   0.0011843016 140.9984983418
## African.Americans.in.Tract   0.0001396996   0.0009954077 142.7064406955
## Noncitizens.in.Tract         0.0008178698   0.0007358216 114.7156862767
## High.BP                      0.0002412119   0.0001827021  86.6878878144
## Binge.Drinking               0.0001272692   0.0001459184  34.3261620538
## Cancer                      -0.0007972244   0.0010497815  82.9691603894
## Asthma                       0.0004853341   0.0005090788  31.1164766873
## Heart.Disease                0.0005965888   0.0012220823  60.9601033416
## COPD                         0.0000268897   0.0010322652  63.9445400109
## Smoking                     -0.0001469246   0.0002178419  67.3938169539
## Diabetes                    -0.0004910568   0.0005179398  65.9315500917
## No.Physical.Activity         0.0000165102   0.0001944344  73.0009159092
## Obesity                      0.0001928679   0.0001675970  79.4067946110
## Poor.Sleeping.Habits        -0.0000004496   0.0001615245 116.4925316048
## Poor.Mental.Health          -0.0000325802   0.0003844988  26.7523010992
## Testing_Rate                 0.0000004884   0.0000003230  27.0157178141
## Hospitalization_Rate        -0.0001180858   0.0000815336  25.4805497692
##                            t value  Pr(>|t|)    
## (Intercept)                 -0.872    0.3865    
## Affluence                    4.154 0.0000733 ***
## Singletons.in.Tract          1.864    0.0645 .  
## Seniors.in.Tract             0.792    0.4299    
## African.Americans.in.Tract   0.140    0.8886    
## Noncitizens.in.Tract         1.112    0.2687    
## High.BP                      1.320    0.1902    
## Binge.Drinking               0.872    0.3892    
## Cancer                      -0.759    0.4498    
## Asthma                       0.953    0.3478    
## Heart.Disease                0.488    0.6272    
## COPD                         0.026    0.9793    
## Smoking                     -0.674    0.5023    
## Diabetes                    -0.948    0.3465    
## No.Physical.Activity         0.085    0.9326    
## Obesity                      1.151    0.2533    
## Poor.Sleeping.Habits        -0.003    0.9978    
## Poor.Mental.Health          -0.085    0.9331    
## Testing_Rate                 1.512    0.1421    
## Hospitalization_Rate        -1.448    0.1597    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of fixed effects could have been required in summary()
## 
## Correlation of Fixed Effects:
##             (Intr) Afflnc Sng..T Snr..T A.A..T Nnc..T Hgh.BP Bng.Dr Cancer
## Affluence    0.151                                                        
## Sngltns.n.T -0.007  0.054                                                 
## Snrs.n.Trct  0.586  0.380  0.173                                          
## Afrcn.Am..T  0.187  0.162 -0.432  0.168                                   
## Nnctzns.n.T -0.013  0.097  0.049  0.059 -0.073                            
## High.BP      0.023  0.248  0.096  0.137 -0.107  0.396                     
## Bing.Drnkng -0.246 -0.181 -0.303 -0.174  0.114  0.052  0.147              
## Cancer      -0.597 -0.208  0.181 -0.341 -0.076 -0.155 -0.403 -0.134       
## Asthma      -0.351 -0.200 -0.200 -0.151  0.086  0.092  0.165 -0.014  0.038
## Heart.Dises -0.146  0.077 -0.285 -0.149  0.238 -0.097 -0.033  0.063 -0.460
## COPD         0.551  0.036  0.133  0.269  0.004  0.289  0.220  0.125 -0.267
## Smoking     -0.191  0.101 -0.180 -0.132 -0.090 -0.008 -0.106 -0.297  0.086
## Diabetes     0.055 -0.316 -0.165 -0.241 -0.278 -0.329 -0.526  0.037  0.220
## N.Physcl.Ac -0.175 -0.061  0.104 -0.022 -0.025 -0.228 -0.124  0.085  0.495
## Obesity      0.026  0.442  0.397  0.311  0.163  0.218 -0.063 -0.219  0.105
## Pr.Slpng.Hb -0.501 -0.410  0.181 -0.391 -0.405  0.008 -0.187  0.062  0.176
## Pr.Mntl.Hlt -0.321  0.257 -0.056 -0.066  0.111 -0.205 -0.102  0.036  0.312
## Testing_Rat  0.191 -0.113 -0.090 -0.019  0.042 -0.101 -0.004  0.033 -0.196
## Hsptlztn_Rt -0.115 -0.235 -0.172 -0.259 -0.074 -0.130 -0.147 -0.152  0.047
##             Asthma Hrt.Ds COPD   Smokng Diabts N.Ph.A Obesty Pr.S.H Pr.M.H
## Affluence                                                                 
## Sngltns.n.T                                                               
## Snrs.n.Trct                                                               
## Afrcn.Am..T                                                               
## Nnctzns.n.T                                                               
## High.BP                                                                   
## Bing.Drnkng                                                               
## Cancer                                                                    
## Asthma                                                                    
## Heart.Dises  0.273                                                        
## COPD        -0.361 -0.565                                                 
## Smoking      0.072  0.220 -0.529                                          
## Diabetes    -0.124 -0.233 -0.165  0.294                                   
## N.Physcl.Ac  0.010 -0.397  0.004 -0.349 -0.089                            
## Obesity     -0.279 -0.109  0.189 -0.224 -0.406 -0.051                     
## Pr.Slpng.Hb  0.084  0.244 -0.218  0.043 -0.017 -0.098 -0.172              
## Pr.Mntl.Hlt -0.221  0.096 -0.461  0.084  0.038  0.077  0.091 -0.192       
## Testing_Rat -0.363 -0.021  0.177  0.177  0.160 -0.330  0.053 -0.142 -0.115
## Hsptlztn_Rt  0.009  0.077 -0.104  0.153  0.152 -0.067 -0.146  0.014 -0.017
##             Tstn_R
## Affluence         
## Sngltns.n.T       
## Snrs.n.Trct       
## Afrcn.Am..T       
## Nnctzns.n.T       
## High.BP           
## Bing.Drnkng       
## Cancer            
## Asthma            
## Heart.Dises       
## COPD              
## Smoking           
## Diabetes          
## N.Physcl.Ac       
## Obesity           
## Pr.Slpng.Hb       
## Pr.Mntl.Hlt       
## Testing_Rat       
## Hsptlztn_Rt  0.312
## fit warnings:
## Some predictor variables are on very different scales: consider rescaling
this.lme.sum <- summary(this.lme)